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  <div class="section" id="numpy-mean">
<h1>numpy.mean<a class="headerlink" href="#numpy-mean" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.mean">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">mean</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">keepdims=&lt;no value&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/core/fromnumeric.py#L3231-L3335"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.mean" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the arithmetic mean along the specified axis.</p>
<p>Returns the average of the array elements.  The average is taken over
the flattened array by default, otherwise over the specified axis.
<code class="xref py py-obj docutils literal notranslate"><span class="pre">float64</span></code> intermediate and return values are used for integer inputs.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Array containing numbers whose mean is desired. If <em class="xref py py-obj">a</em> is not an
array, a conversion is attempted.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">None or int or tuple of ints, optional</span></dt><dd><p>Axis or axes along which the means are computed. The default is to
compute the mean of the flattened array.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.7.0.</span></p>
</div>
<p>If this is a tuple of ints, a mean is performed over multiple axes,
instead of a single axis or all the axes as before.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">data-type, optional</span></dt><dd><p>Type to use in computing the mean.  For integer inputs, the default
is <code class="xref py py-obj docutils literal notranslate"><span class="pre">float64</span></code>; for floating point inputs, it is the same as the
input dtype.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternate output array in which to place the result.  The default
is <code class="docutils literal notranslate"><span class="pre">None</span></code>; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
See <em class="xref py py-obj">ufuncs-output-type</em> for more details.</p>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.</p>
<p>If the default value is passed, then <em class="xref py py-obj">keepdims</em> will not be
passed through to the <a class="reference internal" href="#numpy.mean" title="numpy.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a> method of sub-classes of
<a class="reference internal" href="numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a>, however any non-default value will be.  If the
sub-class’ method does not implement <em class="xref py py-obj">keepdims</em> any
exceptions will be raised.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>m</strong><span class="classifier">ndarray, see dtype parameter above</span></dt><dd><p>If <em class="xref py py-obj">out=None</em>, returns a new array containing the mean values,
otherwise a reference to the output array is returned.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.average.html#numpy.average" title="numpy.average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">average</span></code></a></dt><dd><p>Weighted average</p>
</dd>
</dl>
<p><a class="reference internal" href="numpy.std.html#numpy.std" title="numpy.std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">std</span></code></a>, <a class="reference internal" href="numpy.var.html#numpy.var" title="numpy.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a>, <a class="reference internal" href="numpy.nanmean.html#numpy.nanmean" title="numpy.nanmean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmean</span></code></a>, <a class="reference internal" href="numpy.nanstd.html#numpy.nanstd" title="numpy.nanstd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanstd</span></code></a>, <a class="reference internal" href="numpy.nanvar.html#numpy.nanvar" title="numpy.nanvar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanvar</span></code></a></p>
</div>
<p class="rubric">Notes</p>
<p>The arithmetic mean is the sum of the elements along the axis divided
by the number of elements.</p>
<p>Note that for floating-point input, the mean is computed using the
same precision the input has.  Depending on the input data, this can
cause the results to be inaccurate, especially for <code class="xref py py-obj docutils literal notranslate"><span class="pre">float32</span></code> (see
example below).  Specifying a higher-precision accumulator using the
<a class="reference internal" href="numpy.dtype.html#numpy.dtype" title="numpy.dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></a> keyword can alleviate this issue.</p>
<p>By default, <code class="xref py py-obj docutils literal notranslate"><span class="pre">float16</span></code> results are computed using <code class="xref py py-obj docutils literal notranslate"><span class="pre">float32</span></code> intermediates
for extra precision.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">2.5</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([2., 3.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([1.5, 3.5])</span>
</pre></div>
</div>
<p>In single precision, <a class="reference internal" href="#numpy.mean" title="numpy.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a> can be inaccurate:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">512</span><span class="o">*</span><span class="mi">512</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">0.54999924</span>
</pre></div>
</div>
<p>Computing the mean in float64 is more accurate:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">0.55000000074505806 # may vary</span>
</pre></div>
</div>
</dd></dl>

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